Load required packages

library(tidyverse)
library(phyloseq)
library(speedyseq)
library(plotly)
options(getClass.msg=FALSE) # https://github.com/epurdom/clusterExperiment/issues/66
#this fixes an error message that pops up because the class 'Annotated' is defined in two different packages

Load functions from Github

'%!in%' <- function(x,y)!('%in%'(x,y))

source("https://raw.githubusercontent.com/fconstancias/DivComAnalyses/master/R/phyloseq_taxa_tests.R")
source("https://raw.githubusercontent.com/fconstancias/DivComAnalyses/master/R/phyloseq_normalisation.R")
## Loading required package: scales
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
## Loading required package: reshape2
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
source("https://raw.githubusercontent.com/fconstancias/DivComAnalyses/master/R/phyloseq_alpha.R")
source("https://raw.githubusercontent.com/fconstancias/DivComAnalyses/master/R/phyloseq_beta.R")
source("https://raw.githubusercontent.com/fconstancias/DivComAnalyses/master/R/phyloseq_heatmap.R")

Load physeq object

ps = "data/processed/physeq_update_23_11.RDS"

ps %>% 
  here::here() %>%
  readRDS() %>%
  phyloseq_get_strains_fast() %>%
  phyloseq_remove_chloro_mitho() -> physeq
## Joining, by = "ASV"
physeq
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 346 taxa and 384 samples ]
## sample_data() Sample Data:       [ 384 samples by 50 sample variables ]
## tax_table()   Taxonomy Table:    [ 346 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 346 tips and 344 internal nodes ]
## refseq()      DNAStringSet:      [ 346 reference sequences ]
physeq@sam_data %>%
  data.frame() %>%
  rownames_to_column('id') %>%
  left_join(
    "data/raw/hplc Fermentation (Salvato automaticamente).xlsx" %>%
      readxl::read_xlsx(sheet = "All total"),
    by = c("Sample_description" = "Sample_Id")) %>%
  column_to_rownames('id') %>% 
  sample_data() -> physeq@sam_data
physeq
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 346 taxa and 384 samples ]
## sample_data() Sample Data:       [ 384 samples by 63 sample variables ]
## tax_table()   Taxonomy Table:    [ 346 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 346 tips and 344 internal nodes ]
## refseq()      DNAStringSet:      [ 346 reference sequences ]

We will be analyzing only the PolyFermS samples here so take a subset of the physeq object.

physeq %>% 
  subset_samples(Experiment == "Continuous") %>% 
  subset_samples(Paul %!in% c("Paul")) %>%
  subset_samples(Reactor != "IR2") -> ps_polyFermS

sample_data(ps_polyFermS)$Reactor <- fct_relevel(sample_data(ps_polyFermS)$Reactor, "IR1", "CR", "TR1", "TR2","TR3", "TR4", "TR5", "TR6") 

sample_data(ps_polyFermS)$Treatment <- fct_relevel(sample_data(ps_polyFermS)$Treatment, "UNTREATED",  "CTX+HV292.1", "CTX","HV292.1","VAN+CCUG59168", "VAN",  "CCUG59168") 

sample_data(ps_polyFermS)$Reactor_Treatment <- fct_relevel(sample_data(ps_polyFermS)$Reactor_Treatment, "IR1_UNTREATED","CR_UNTREATED", "CR_CTX", "CR_VAN", "TR1_CTX+HV292.1","TR2_CTX", "TR3_HV292.1", "TR5_VAN+CCUG59168", "TR4_VAN", "TR6_CCUG59168") 
ps_polyFermS %>%
  sample_data() %>%
  data.frame() -> df
measures = df %>% select(ends_with("mM")) %>% colnames()

# define a function to plot scatter plot
my_fn <- function(data, mapping, ...){
  p <- ggplot(data = data, mapping = mapping) +
    geom_point() +
    geom_smooth(method=lm, ...)
  p
}


df %>%
  GGally::ggpairs(columns = measures,
                  ggplot2::aes(colour = Reactor),
                  # legend = 1,
                  progress = FALSE,
                  upper = list(
                    continuous = GGally::wrap('cor', method = "pearson")
                  ),
                  lower = list(continuous = my_fn)) -> p_corr

p_corr

df %>%
  plot_alphas(measure = measures,
              x_group = "Reactor_Treatment",
              colour_group = "Enrichment",
              fill_group = "Enrichment",
              shape_group = "Enrichment",
              facet_group = "Reactor_Treatment",
              test_group = "Reactor_Treatment",
              test_group_2 = "Enrichment") -> out
plot_alpha_time <- function(df, 
                            x = "Day_from_Inoculum", 
                            y = "value", 
                            shape = "neg",
                            fill = "Reactor_Treatment",
                            group = "Reactor_Treatment", 
                            facet)
{
  df %>%
  arrange(Day_from_Inoculum) %>%
  ggplot(aes_string(x = x,
             y = y, shape = shape)) +
  geom_point(size=2, alpha=0.9, aes_string(group = group, color = fill, fill = fill),  show.legend = FALSE) + 
  geom_path(inherit.aes = TRUE, aes_string(group=group),
            size = 0.08,
            linetype = "dashed") +
  facet_grid(as.formula(facet), scales = "free") +
  theme_light() +
  scale_color_viridis_d(na.value = "black") + 
  geom_vline(xintercept = c(23,39), 
             color="black", alpha=0.4) + 
  # geom_smooth(show.legend = TRUE, level = 0.95) + 
  scale_x_continuous(breaks=seq(0,90,10)) -> plot

  return(plot)
}
out$plot$data %>%
  dplyr::filter(alphadiversiy == "Total_SCFA_mM") %>%
  dplyr::mutate(neg = ifelse(value == 0, "neg", "pos")) %>%
  arrange(Day_from_Inoculum) %>%
  ggplot(aes_string(x = "Day_from_Inoculum",
                    y = "value", group = "Reactor_Treatment")) +
  geom_jitter(size=0.5, alpha=0.9, aes_string(group = "Reactor_Treatment", color = "Reactor_Treatment", fill = "Reactor_Treatment"),  show.legend = TRUE) + 
  geom_path(inherit.aes = TRUE, aes_string(group="Reactor_Treatment", fill = "Reactor_Treatment", color = "Reactor_Treatment", show.legend = FALSE),
            size = 0.001,
            linetype = "dashed") +
  # facet_grid(as.formula(facet), scales = "free") +
  geom_vline(xintercept = c(23,39), 
             color="black", alpha=0.4) + 
  geom_smooth(show.legend = FALSE, level = 0.95, alpha=0.05, size = 0.5 ,aes_string(group="Reactor_Treatment", color = "Reactor_Treatment", fill = "Reactor_Treatment")) +
  scale_x_continuous(breaks=seq(0,90,10)) +
  # scale_y_continuous(labels = scientific,
  #                    limits=c(1e+10, 1e+11), breaks = seq(1e+10, 1e+11, by = 1e+10),
  #                    trans = "log10") +
  labs(x="Day (from Inoculum)", y= "SCFA concentration [mM]",  
       col=NULL, fill = NULL, shape = NULL) +
  theme_light() +
  scale_color_viridis_d(na.value = "black") +
  scale_fill_viridis_d(na.value = "black") -> plot
## Warning: Ignoring unknown aesthetics: fill, show.legend
plot + theme(legend.position = "bottom")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

plot %>%
  plotly::ggplotly()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
plot_time <- function(df, 
                      measure,
                      x = "Day_from_Inoculum", 
                      y = "value", 
                      shape = "neg",
                      fill = "Reactor_Treatment",
                      group = "Reactor_Treatment", 
                      facet)
{
  df %>%
  dplyr::filter(alphadiversiy %in% measure) %>%
  dplyr::mutate(alphadiversiy = fct_reorder(alphadiversiy, value, .desc = TRUE)) %>%
  dplyr::mutate(neg = ifelse(value == 0, "neg", "pos")) %>%
  arrange(Day_from_Inoculum) %>%
  ggplot(aes_string(x = x,
                    y = y)) +
  geom_jitter(size=0.5, alpha=0.9, aes_string(color = fill, fill = fill, shape = shape),  show.legend = TRUE) + 
  geom_path(inherit.aes = TRUE, aes_string(fill = fill, color = fill, show.legend = FALSE),
            size = 0.005,
            linetype = "dashed") +
  facet_grid(as.formula(facet), scales = "free") +
  geom_vline(xintercept = c(23,39), 
             color="black", alpha=0.4) + 
  geom_smooth(show.legend = FALSE, level = 0.95, alpha=0.05, size = 0.5 ,aes_string(color = fill, fill = fill)) +
  scale_x_continuous(breaks=seq(0,90,10)) +
  # scale_y_continuous(labels = scientific,
  #                    limits=c(1e+10, 1e+11), breaks = seq(1e+10, 1e+11, by = 1e+10),
  #                    trans = "log10") +
  theme_light() +
  scale_color_viridis_d(na.value = "black") +
  scale_fill_viridis_d(na.value = "black") -> plot

  return(plot + theme(legend.position = "bottom"))
}
out$plot$data %>%
  plot_time(measure = c("Total_SCFA_mM", "Acetat_mM", "Butyrat_mM", "Propionat_mM", "Isobutyrat_mM", "Valerat_mM", "Isovalerat_mM", "Succinat_mM"),
            facet = c("alphadiversiy ~ ."),  shape = NULL) + 
  labs(x="Day (from Inoculum)", y= "SCFA concentration [mM]",  
       col=NULL, fill = NULL, shape = NULL) + 
  scale_shape_manual(values=c(4, 19)) -> p4

p4

p4 %>% ggplotly()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
htmlwidgets::saveWidget(as_widget(p4 %>% ggplotly()), 
  paste0(here::here(),
                    "/data/processed/",
       "metabolites_",
       format(Sys.time(), "%Y%b%d"),".html"))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# htmlwidgets::saveWidget(as_widget(p4 %>% ggplotly()), paste0("~/Documents/index.html"))
p4 + 
  facet_null() +
  facet_grid(alphadiversiy ~ Reactor_Treatment, scales = "free") +
  scale_color_manual(values = rep("black",8)) +
  scale_fill_manual(values = rep("black",8)) 

p4 + 
  facet_null() +
  facet_grid(Reactor_Treatment  ~ alphadiversiy, scales = "free") +
  scale_color_manual(values = rep("black",8)) +
  scale_fill_manual(values = rep("black",8)) + 
  scale_x_continuous(breaks=seq(0,90,20))

df %>%
  dplyr::select(ends_with("mM") | "Total_SCFA_mM") %>%
  drop_na() %>%
  # t() %>%
  scale(center = TRUE, 
        scale = TRUE) %>%
  dist(method= "euc") -> euc_met

plot_ordination(ps_polyFermS,
                ordination = phyloseq::ordinate(ps_polyFermS,
                                      distance = euc_met, 
                                      method = "PCoA")) -> pca

pca$layers[[1]] = NULL

pca +
  geom_point(size=2,
                   aes(color = Reactor_Treatment, 
                       fill = NULL,
                       shape = NULL,
                       alpha = Day_from_Inoculum)) + 
  theme_light() +
  geom_path(arrow = arrow(type = "open", angle = 30, length = unit(0.15, "inches")),
              size = 0.08, linetype = "dashed", inherit.aes = TRUE, aes(group=Reactor_Treatment, color = Reactor_Treatment)) +
  scale_alpha_continuous(range=c( 0.9, 0.3)) + 
  scale_color_viridis_d(na.value = "red") + 
  scale_fill_viridis_d(na.value = "red") + 
  scale_shape_manual(values = c(8, 21, 22, 23, 24, 16, 15, 18, 17)) + 
  theme_classic() -> p5

p5

p5 %>%
  plotly::ggplotly() -> p5ly

htmlwidgets::saveWidget(as_widget(p5ly), 
  paste0(here::here(),
                    "/data/processed/",
       "metabolites_",
       format(Sys.time(), "%Y%b%d"),"_2.html"))
paste0(here::here(),
       "/data/processed/",
       "metabolites",
       "_",
       format(Sys.time(), "%Y%b%d")
       ,".RData") %>% save.image()
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] reshape2_1.4.4  scales_1.1.1    plotly_4.9.2.2  speedyseq_0.3.0
##  [5] phyloseq_1.30.0 forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
##  [9] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
## [13] ggplot2_3.3.3   tidyverse_1.3.0
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-0    ggsignif_0.6.0      ellipsis_0.3.1     
##   [4] rio_0.5.16          rprojroot_2.0.2     XVector_0.26.0     
##   [7] fs_1.5.0            rstudioapi_0.13     ggpubr_0.4.0       
##  [10] farver_2.0.3        fansi_0.4.1         lubridate_1.7.9.2  
##  [13] xml2_1.3.2          codetools_0.2-18    splines_3.6.3      
##  [16] knitr_1.30          ade4_1.7-16         jsonlite_1.7.2     
##  [19] broom_0.7.3         cluster_2.1.0       dbplyr_2.0.0       
##  [22] compiler_3.6.3      httr_1.4.2          backports_1.2.1    
##  [25] assertthat_0.2.1    Matrix_1.3-0        lazyeval_0.2.2     
##  [28] cli_2.2.0           htmltools_0.5.0     prettyunits_1.1.1  
##  [31] tools_3.6.3         igraph_1.2.6        gtable_0.3.0       
##  [34] glue_1.4.2          Rcpp_1.0.5          carData_3.0-4      
##  [37] Biobase_2.46.0      cellranger_1.1.0    vctrs_0.3.6        
##  [40] Biostrings_2.54.0   multtest_2.42.0     ape_5.4-1          
##  [43] nlme_3.1-151        crosstalk_1.1.0.1   iterators_1.0.13   
##  [46] xfun_0.19           openxlsx_4.2.3      rvest_0.3.6        
##  [49] lifecycle_0.2.0     rstatix_0.6.0       zlibbioc_1.32.0    
##  [52] MASS_7.3-53         hms_0.5.3           parallel_3.6.3     
##  [55] biomformat_1.14.0   rhdf5_2.30.1        RColorBrewer_1.1-2 
##  [58] yaml_2.2.1          curl_4.3            reshape_0.8.8      
##  [61] stringi_1.5.3       S4Vectors_0.24.4    foreach_1.5.1      
##  [64] permute_0.9-5       BiocGenerics_0.32.0 zip_2.1.1          
##  [67] rlang_0.4.10        pkgconfig_2.0.3     evaluate_0.14      
##  [70] lattice_0.20-41     Rhdf5lib_1.8.0      htmlwidgets_1.5.3  
##  [73] labeling_0.4.2      tidyselect_1.1.0    here_1.0.1         
##  [76] GGally_2.1.0        plyr_1.8.6          magrittr_2.0.1     
##  [79] R6_2.5.0            IRanges_2.20.2      generics_0.1.0     
##  [82] DBI_1.1.0           pillar_1.4.7        haven_2.3.1        
##  [85] foreign_0.8-75      withr_2.3.0         mgcv_1.8-33        
##  [88] abind_1.4-5         survival_3.2-7      modelr_0.1.8       
##  [91] crayon_1.3.4        car_3.0-10          rmarkdown_2.6      
##  [94] progress_1.2.2      grid_3.6.3          readxl_1.3.1       
##  [97] data.table_1.13.6   vegan_2.5-7         reprex_0.3.0       
## [100] digest_0.6.27       stats4_3.6.3        munsell_0.5.0      
## [103] viridisLite_0.3.0